Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61190
Title: Occupancy data analytics and prediction : a case study
Authors: Liang, X
Hong, T
Shen, GQ 
Keywords: Data mining
Machine learning
Occupancy prediction
Occupant presence
Issue Date: 2016
Publisher: Pergamon Press
Source: Building and environment, 2016, v. 102, p. 179-192 How to cite?
Journal: Building and environment 
Abstract: Occupants are a critical impact factor of building energy consumption. Numerous previous studies emphasized the role of occupants and investigated the interactions between occupants and buildings. However, a fundamental problem, how to learn occupancy patterns and predict occupancy schedule, has not been well addressed due to highly stochastic activities of occupants and insufficient data. This study proposes a data mining based approach for occupancy schedule learning and prediction in office buildings. The proposed approach first recognizes the patterns of occupant presence by cluster analysis, then learns the schedule rules by decision tree, and finally predicts the occupancy schedules based on the inducted rules. A case study was conducted in an office building in Philadelphia, U.S. Based on one-year observed data, the validation results indicate that the proposed approach significantly improves the accuracy of occupancy schedule prediction. The proposed approach only requires simple input data (i.e., the time series data of occupant number entering and exiting a building), which is available in most office buildings. Therefore, this approach is practical to facilitate occupancy schedule prediction, building energy simulation and facility operation.
URI: http://hdl.handle.net/10397/61190
ISSN: 0360-1323
EISSN: 1873-684X
DOI: 10.1016/j.buildenv.2016.03.027
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